Validation Methods for Multiplexed Imaging Method

ABSTRACT

A quantitative method of validating at least one candidate imaging method or candidate imaging reagent for use in evaluating a biological sample for the presence of one or more targets is described relying upon cross-correlation calculations.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority of US ProvisionalApplication Nos. 62/596,587, filed Dec. 8, 2017, and 62/630,405, filedFeb. 14, 2018, the contents of all of which are incorporated byreference herein in their entirety for any purpose.

FIELD

This application relates generally to the field of detection andquantification of analytes (e.g., targets) and the comparison ofdifferent imaging reagents and methods.

BACKGROUND

Imaging of biological samples is useful in the diagnosis, treatment, andmanagement of many diseases, including, for example, cancer. Commonimaging methods include fluorescence microscopy, brightfield microscopy,electron microscopy and mass spectrometry imaging. Not every imagingmethod suits each situation and investigators can be required to switchbetween imaging methods. It is useful to determine the degree ofsimilarity between results of different imaging methods. When a newmethod is reasonably comparable to an accepted method, it is often saidto be validated against the accepted method. Additionally, investigatorsoften desire to validate new reagents for use in imaging methods.

The present application describes a method for comparing imaging methodsand reagents that is quantitative, capable of automation, streamlined,and, in some embodiments, does not require multiple staining steps thatcould alter the conditions of the biological sample and reduce theeffectiveness of the comparison.

SUMMARY

In accordance with the description, a quantitative method of validatingat least one candidate imaging method or candidate imaging reagent foruse in evaluating a biological sample for the presence of one or moretargets comprises:

-   -   a. obtaining a first imaging signal using a first imaging method        and/or first imaging reagents comprising:        -   i. contacting a biological sample with one or more            target-specific binding partners, wherein each            target-specific binding partner is linked to a nucleic acid            strand and wherein target-specific binding partners of            different specificity, if present, are linked to different            nucleic acid strands, wherein the nucleic acid strand is            either a docking strand or a primer strand for amplification            of docking strands;        -   ii. contacting the biological sample with labeled imager            strands for a first imaging method, wherein the labeled            imager strands are capable of binding a docking strand,            directly or indirectly,        -   iii. generating a first imaging signal;    -   b. optionally removing the bound labeled imager strands from the        docking strands;    -   c. obtaining a second imaging signal using a second imaging        method and/or second imaging reagents comprising contacting the        biological sample with either (1) labeled imager strands,        wherein the labeled imager strands are capable of binding a        docking strand, directly or indirectly, or (2) a secondary        binding partner for the target-specific binding partner,    -   wherein the second imaging method and/or the second imaging        reagent is different than the first imaging method and/or first        imaging reagent;    -   d. optionally aligning the first imaging signal and the second        imaging signal to adjust for signal orientation, image parity,        scale, rotation, and/or translation mismatch,    -   e. identifying a first imaging zone in both the first imaging        signal and second imaging signal, wherein the first imaging zone        in both imaging signals represents the same location in the        biological sample;    -   f. comparing the first imaging zone in the first imaging signal        and the first imaging zone in the second imaging signal by        performing a cross-correlation.

In some embodiments, a quantitative method of validating at least onecandidate imaging method or candidate imaging reagent for use inevaluating a biological sample for the presence of one or more targetscomprises:

-   -   a. obtaining a first imaging signal using a first imaging method        and/or first imaging reagents,    -   b. obtaining a second imaging signal using a second imaging        method and/or second imaging reagents,    -   wherein the second imaging method and/or the second imaging        reagent is different the first imaging method and/or first        imaging reagent;    -   c. optionally aligning the first imaging signal and the second        imaging signal to adjust for signal orientation, image parity,        scale, rotation, and/or translation mismatch,    -   d. identifying a first imaging zone in both the first imaging        signal and second imaging signal, wherein the first imaging zone        in both imaging signals represents the same location in the        biological sample;    -   e. comparing the first imaging zone in the first imaging signal        and the first imaging zone in the second imaging signal by        performing a cross-correlation.

Additional objects and advantages will be set forth in part in thedescription which follows, and in part will be obvious from thedescription, or can be learned by practice. The objects and advantageswill be realized and attained by means of the elements and combinationsparticularly pointed out in the appended claims.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the claims.

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate one (several) embodiment(s) andtogether with the description, explain the principles described herein.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

FIG. 1 provides a first imaging signal from a fluorescent stain of abiological sample.

FIG. 2 shows a second imaging signal from a chromogenic stain of thesame biological sample as shown in FIG. 1.

FIG. 3 shows the second imaging signal shown in FIG. 2 after imageadjustments steps to align with the first imaging signal in FIG. 1.

FIG. 4 shows the first imaging signal from FIG. 1 after optionallycropping the bounds of the first imaging signal to the bounds of theprocessed second imaging signal of FIG. 3.

FIG. 5 shows an example first imaging zone (A) and an example secondimaging zone (B) overlaid on the chromogenic stain of FIG. 2 afteralignment. The black lines at A and B show the bounds of the exampleimaging zones in the first imaging signal (the fluorescent stain of FIG.4) and the white lines at A and B show the bounds of the imaging zonesin the second imaging signal (the chromogenic stain shown in FIG. 2 andFIG. 3).

FIGS. 6A-D show the details of imaging zones A and B from the firstimaging signal (Image 1) and the second imaging signal (Image 2).

FIGS. 7A-D show the results of normalized cross-correlation images fordifferent combinations of the example imaging zones A and B.

DESCRIPTION OF THE EMBODIMENTS I. Quantitative Methods for ValidatingCandidate Imaging Methods or Candidate Imaging Reagents

The present application describes a quantitative method for comparingone or more candidate imaging methods or one or more candidate imagingreagents. The comparisons between methods can be made in a way thatrelies on computer processing to account for differences in the waydifferent imaging methods report data or the view of any giveninstrument. Thus, this provides a robust and reliable way to evaluatedifferent reagents and methods.

A quantitative method of validating at least one candidate imagingmethod or candidate imaging reagent for use in evaluating a biologicalsample for the presence of one or more targets comprises:

-   -   a. obtaining a first imaging signal using a first imaging method        and/or first imaging reagents comprising:        -   i. contacting a biological sample with one or more            target-specific binding partners, wherein each            target-specific binding partner is linked to a nucleic acid            strand and wherein target-specific binding partners of            different specificity, if present, are linked to different            nucleic acid strands, wherein the nucleic acid strand is            either a docking strand or a primer strand for amplification            of docking strands;        -   ii. contacting the biological sample with labeled imager            strands for a first imaging method, wherein the labeled            imager strands are capable of binding a docking strand,            directly or indirectly,        -   iii. generating a first imaging signal;    -   b. optionally removing the bound labeled imager strands from the        docking strands;    -   c. obtaining a second imaging signal using a second imaging        method and/or second imaging reagents comprising contacting the        biological sample with either (1) labeled imager strands,        wherein the labeled imager strands are capable of binding a        docking strand, directly or indirectly, or (2) a secondary        binding partner for the target-specific binding partner    -   wherein the second imaging method and/or the second imaging        reagent is different the first imaging method and/or first        imaging reagent;    -   d. optionally aligning the first imaging signal and the second        imaging signal to adjust for signal orientation, image parity,        scale, rotation, and/or translation mismatch,    -   e. identifying a first imaging zone in both the first imaging        signal and second imaging signal, wherein the first imaging zone        in both imaging signals represents the same location in the        biological sample;    -   f. comparing the first imaging zone in the first imaging signal        and the first imaging zone in the second imaging signal by        performing a cross-correlation.

In some embodiments, the quantitative method does not employ atarget-specific binding partner linked to a nucleic acid strand or alabeled imager strand, but instead employs a target-specific bindingpartner that is not linked to a nucleic acid strand. Thus, aquantitative method of validating at least one candidate imaging methodor candidate imaging reagent for use in evaluating a biological samplefor the presence of one or more targets comprises:

-   -   a. obtaining a first imaging signal using a first imaging method        and/or first imaging reagents,    -   b. obtaining a second imaging signal using a second imaging        method and/or second imaging reagents,    -   wherein the second imaging method and/or the second imaging        reagent is different the first imaging method and/or first        imaging reagent;    -   c. optionally aligning the first imaging signal and the second        imaging signal to adjust for signal orientation, image parity,        scale, rotation, and/or translation mismatch,    -   d. identifying a first imaging zone in both the first imaging        signal and second imaging signal, wherein the first imaging zone        in both imaging signals represents the same location in the        biological sample;    -   e. comparing the first imaging zone in the first imaging signal        and the first imaging zone in the second imaging signal by        performing a cross-correlation.

In such an embodiment, the first imaging method can employ a firsttargeting antibody bound directly or indirectly to a first label and thesecond imaging method can employ a second targeting antibody bounddirectly or indirectly to a second label. In some embodiments, one orboth targeting antibodies are bound directly to their respective label.

The first imaging signal and the second imaging signal are from the samemicroscope slide and include at least some overlapping areas from thesame microscope slide. In some embodiments, the areas are entirelyoverlapping and in other embodiments they are not entirely overlapping.

In some embodiments, cross-correlation comprises generating numericaldata. In some embodiments, cross-correlation comprises generating animage representing the numerical data.

The cross-correlation can comprise identifying the peakcross-correlation between the first imaging zone in the first imagingsignal and the first imaging zone in the second imaging signal. Thecross-correlation can also comprise evaluating the breadth of the peakcross-correlation. The cross-correlation can, in some embodiments,comprise identifying and comparing secondary peaks and/or measuring thebackground of the cross-correlation.

The method can be used to compare two candidate imaging methods. Themethod can be used to compare two candidate imaging reagents. In someembodiments, the method can be used to compare more than two candidateimaging methods. In some embodiments, the method can be used to comparemore than two candidate imaging reagents. For example, the method can beused to compare 3, 4, 5, 6, 7, 8, 9, 10 or more candidate imagingmethods or candidate imaging reagents.

Multiple candidate imaging methods or candidate imaging reagents can becompared by evaluating each candidate imaging method or candidateimaging reagent to a control (a previously used candidate imaging methodor candidate imaging reagent) and/or by comparing each candidate imagingmethod or candidate imaging reagent to at least one other (one, some, orall) candidate imaging method or candidate imaging reagent.

A. Aligning the First Imaging Signal and the Second Imaging Signal

When the first imaging signal and the second imaging signal aregenerated using different modalities (for example, fluorescence imagingand brightfield imaging) or even when the first imaging signal and thesecond imaging signal are generated using different pieces of equipment,or when a sample is removed for further processing and reloaded, theuser may need to align the first imaging signal and the second imagingsignal before validation can take place. When the first imaging signaland the second imaging signal are generated using the same modality (forexample, all fluorescent imaging) and/or optionally when they aregenerated using the same instrument and/or optionally when the sample isnot removed and replaced from the field of view, it might not benecessary to perform any steps of aligning the first imaging signal andthe second imaging signal. Additionally, when different modalities areused, different instruments are used, or when an operator removes andreplaces the sample, the properties of the first imaging signal and thesecond imaging signal dictate which alignment steps should occur.

Through this process, either the first imaging signal is aligned to thesecond imaging signal or the second imaging signal is aligned to thefirst imaging signal (i.e., the alignment can be performed in eitherdirection). The user can select the directionality of the alignmentbased on how many images are obtained from a given modality orinstrument or depending on which type of alignment is more convenientfor the user. For example, it can be more convenient to align a coloredimage to make it monochromatic but the image alignment steps can beperformed in either direction (first signal to second signal or viceversa) and in any order. Additionally, it may be possible to align someaspects of the first signal to the second signal (for example changingthe first signal, a colored image, to align to the monochromatic secondsignal), while aligning other aspects of the second signal to the firstsignal (for example to correct the image parity of the second signal tomatch the first signal).

The alignment steps described below could be undertaken in any orderfrom the order presented here. For example, the steps for correcting forscale and rotation could be taken first and converting color spaces andmatching for signal orientation could follow, but this might requireadditional computations (e.g. rotating a color image rather than amonochromatic one) or additional complexity (e.g. rotating backwardsbecause one image has the opposite parity of the other). Similarly, itwould also be possible to perform the alignment after selecting imagingzones for validation instead of the order presented here, but that ordermight require additional complexity in the identification of matchingregions seen in unaligned images.

1. Signal Orientation and Color Spaces

Various forms of imaging produce signals with different meaning,referred to herein as orientation, to the value of the digital signalmeasured by the image detector. For example, in brightfield imaging, thebackground (no signal) is white (high data value) and when the signal ispresent, the image is darker (low data value). In fluorescence imaging,on the other hand, the background is dark (low data value), and regionswith signal are bright (high data value). If two images have oppositeorientations, their orientations can be aligned by inverting theorientation of one to match the other.

To adjust the orientation of an image, the maximum signal in the image(or the maximum possible data value produced by the detector) isdetermined; and all the pixel values are replaced in the image with themaximum minus the measured value. While it is possible to compare imageswith opposite orientations, one would either (1) need to instead lookfor an anti-correlation, rather than a correlation of the data values(e.g., matching a negative image to a positive one), or (2) need tochange the calculation or the correlation to invert one of the sets ofpixel values.

In addition, some imaging modalities, for example bright field imagingof a chromogenic stain, typically produce a multichannel (e.g., RGB)color image. Other modalities, for example fluorescence emissiondetected using interference filters tuned to transmit only a specificrange of wavelengths, produce a monochromatic (single channel) image.Furthermore, in chromogenic imaging a stain of a particular color thatindicates the presence of a signal of interest is examined and othercolors in the stained image are treated as unwanted signals orbackground. Therefore, it is usually necessary to process the colorimage to produce a monochromatic (single channel) image that selects thesignal or color of interest.

To align a color image to a monochromatic one, the RGB (Red, Green,Blue) pixel values are converted to HSV (Hue, Saturation, Value) space(using standard conversions), a range of hue values for the desiredchromogenic stain color (e.g., brown=H˜30±10°) is specified by the user,and a mask image of the pixels in this hue range is created. A maskimage, as used herein, means an intermediate image used to identifypixels with the signal of interest. The mask image has values at eachpixel generated by comparing the values in the initial image to a presetrange and assigning one value in the mask image when the value in theinitial image is within the preset range and another value when thevalue in the initial image is outside of the preset range.

Thus, in embodiments when a binary mask is made, the resulting maskimage is 1 where ever the hue is in this range, and 0 elsewhere. Otherembodiments could create a mask with a real number weight or probabilityvalue at each pixel location ranging from 0.0 to 1.0 indicating thelikelihood that the given pixel represents a signal of interest.Finally, the Value channel of the HSV image is adjusted for orientationas described above and multiplied by the mask image to produce amonochromatic version of the initial color image. Thus, when the maskhas a value of 0 the monochromatic version will also have a value of 0and when the mask has a value of 1 the monochromatic version will havethe value of the orientation adjusted Value channel of the HSV image.

a) Additional Adjustments

If desired, to improve the conversion of a colored image to amonochromatic image, one or more additional steps can be applied atdifferent points in the adjustment or conversion process.

The method of adjusting the image(s) can also optionally includeperforming morphology operations common in image processing (seeen.wikipedia.org/wiki/Mathematical_morphology) on the mask image used toselect pixels which have the desired range of hue values to convert achromogenic image to a monochrome image for a given stain color. Suchoperations can be used to eliminate very small features, to fill holes,smooth edges, etc. Such techniques are known to the person of ordinaryskill in the art.

An intensity scaling step can optionally be performed for themonochromatic image. If this step is chosen by the user, the output canbe scaled such that the smallest value in the mask region is zero andthe largest value is 255 (or whatever is the maximum possible value) inthe output. This scaling can be optionally included to improve thebrightness and contrast of the image in later visualizations.

Although the methods are described herein with respect to HSV, othercolor spaces (e.g., HSL, Hue Saturation Lightness) can be used.Alternatively, the color image can be converted to luminance (a linearcombination of the RGB value of each pixel that produces a grayscalefrom the color image), or a single channel could be selected (e.g.,select the blue channel for a blue colored stain).

2. Image Parity

Some instruments image samples from the top, while other instrumentsimage samples from the bottom of the slide, and/or some instrumentscontain optical elements (e.g., lenses or mirrors) that create aleft-right or top-bottom reversal of the captured image. Thesedifferences in instrumentation can create parity misalignment betweenimages captured with different instruments. Image parity refers topotential left-right reversal (reversion) and/or top-bottom reversal(inversion) of the images.

If an image has left-right parity reversal with respect to another, oneimage can be reflected about its vertical axis, by reversing the orderof the pixel values in each row. If an image has top-bottom parityreversal with respect to the other, one image can be reflected about thehorizontal axis, by reversing the order of rows top to bottom.

3. Image Scale, Rotation, and Translation Mismatch

Differences in the optics of two imaging systems and/or the act ofpositioning the biological sample on each instrument, can result indifferences in the scale, rotation, and/or translation (x,ydisplacement) of the images captured on each device. Image scale refersto the pixel size in an image (i.e., how zoomed-in or zoomed-out animage appears). Image rotation refers to whether the image is rotated onthe plane of imaging by any number of degrees. And image translationmismatch refers to whether the image is shifted up/down or left/right inthe field of view between the first imaging signal and the secondimaging signal.

There are a variety of ways to match the geometry of one image toanother as this is a common problem in microscopy, computer vision andpattern recognition. One method is to first identify one or more (x,y)points in one of the images that match the same features of the sampleas seen in another image and then adjust the scale, rotation andtranslation of one image so the points of one image match the other. Thematching points can be identified using any method, including visualanalysis and an automatically technique such as SURF (Speeded-Up RobustFeatures) (Bay, H., et al., “SURF: Speeded Up Robust Features,” ComputerVision and Image Understanding 110:346-359 (2008)), or SIFT(Scale-Invariant Feature Transform) (D. Lowe, “Distinctive ImageFeatures from Scale-Invariant Keypoints” International J. of ComputerVision 60:91 (2004)).

To align the geometry of one image to that of the other image, an Affinetransformation of the form is performed

${\begin{bmatrix}a_{00} & a_{01} & b_{00} \\a_{10} & a_{11} & b_{10} \\0 & 0 & 1\end{bmatrix}\begin{bmatrix}x \\y \\1\end{bmatrix}} = {\begin{bmatrix}x^{\prime} \\y^{\prime} \\1\end{bmatrix}.}$

where (x, y) and (x′, y′) are the original and transformed coordinatesrespectively, and the matrix elements a_(ij) and b_(ij) are calculatedfrom the collection of matching points. For example, for a translationoffset, only one pair of matching points is required and the differencesin the x and y coordinates of the points are b₀₀ and b₁₀ respectively.From three pairs of points, all the elements of the Affine transform canbe calculated directly using linear algebra. For more than three points,the method of least squares or other parameter estimation techniques canbe used.

In some embodiments, matching point pairs alone are used to calculatethe complete Affine transform to align the geometry of the images. Insome embodiments, matching point pairs are used to calculate the a_(ij)matrix elements, the image is corrected for scale and/or rotation, andthen the method of cross-correlation (described below) is used to findb_(ij), the translation offset for the final transform.

B. Selection and Properties of the Imaging Zones

As part of the validation process, the user selects an imaging zone inthe first imaging signal and an imaging zone in the second imagingsignal. An imaging zone is the portion of an imaging signal selected tocompare to another imaging signal (or another imaging zone in the sameimaging signal). The selection of the imaging zones can occur in anautomated way or it can rely upon user input. A variety of factors canbe used to select an imaging zone, depending on the user's preferences,the contents of a biological sample, or the candidate imaging methodand/or candidate imaging reagent.

An imaging zone can be selected so that it comprises areas of bothsignal and background. In some embodiments, the user can choose(manually or in an automated way) an imaging zone because it comprisesthe strongest signal from the biological sample or because it representsthe background or lowest signal from the biological sample. On the otherhand, an imaging zone can also be chosen randomly.

In some embodiments, an imaging zone contains at least 2, 3, 4, 5, 6, 7,8, 9, 10, 11, 12, 13, 14, 15 or more elements (e.g., cells or distinctobjects in the image). In some embodiments, the imaging zone is at least2, 5, 10, 15, 20, 25, or 30 or more times the size of an average cell inthe biological sample. In some embodiments, an imaging zone can comprisefrom tens to thousands of pixels in area, for example at least 10, 50,100, 250, 500, 750, 1000, 2000, 3000, 5000 pixels or more. In someembodiments, an imaging zone can comprise all or nearly all of an image.

In some embodiments, only one imaging zone is compared between the firstand second imaging signals. In some embodiments, at least one imagingzone in the first imaging signal is compared with the entire biologicalsample (or biological sample viewable in the image) in the secondimaging signal. In some embodiments, two imaging zones are comparedusing this method. In some embodiments, more than two imaging zones areused. For example, in some embodiments, the method relies uponinformation from two or more imaging zones, for example 2, 3, 4, 5, 6,7, 8, 9, 10, or more imaging zones.

C. Validating Candidate Imaging Methods or Candidate Imaging ReagentsThrough Cross-Correlation

Cross-correlation is useful for quantitatively comparing a first imagingsignal to a second imaging signal or to subsequent imaging signals inthe case of multiple candidate imaging methods or candidate imagingreagents.

In signal processing, cross-correlation is a measure of similarity oftwo series as a function of the displacement of one relative to theother. This is also known as a sliding dot product or slidinginner-product. As an example, consider two real-valued functions ƒ (x,y)and g (x,y) differing only by an unknown shift along the x-axis andy-axis. One can use the cross-correlation to find how much g must beshifted along the x and y axes to make it identical to ƒ. The process ofcross-correlation essentially slides the g function along the x and yaxes, calculating the integral of their product at each position. Whenthe functions match, the value of the cross-correlation is maximized.This is because when peaks (positive areas) of one function are alignedwith the other, they make a large contribution to the integral. When apeak in one function is not aligned with a peak in the other, thecross-correlation product will be much lower. In the case where ƒ (x,y)and g (x,y) are not just copies of each other shifted by unknown offset,but where there are also differences between ƒ and g which one wants tomeasure, the value of the maximum cross-correlation of ƒ and g is ameasure of the similarity of the two functions and the position of thatmaximum in x and y is the offset which produces the best match.

1. Cross-Correlating Imaging Zones to Validate the Candidate ImagingMethod and Imaging Zone(s)

To perform the validation, one or more imaging zones (rectangularregions of interest) of Image A are selected for comparison with Image B(wherein Image A can be the first imaging signal and Image B can be thesecond imaging signal or vice versa). Validation can be performed with asingle imaging zone, but for statistical robustness and to studypossible spatial variability of the staining, multiple zones can beused. The size of each zone of Image A, in one embodiment, can be greatenough in area to describe a unique signal pattern. For example, a smallregion around a single cell might easily match the image of many cellsthe other image, so image zones ideally include many to several hundredcells (tens to thousands of pixels in area). A zone can be as large asthe entirety of an image to describe a global or overall likeness ofImage A to Image B, or a zone could be a small fraction of Image A, inwhich case multiple non-overlapping zones could be selected at random,on a regular grid, or centered on features of interest (e.g., thebrightest features) in order to study variability. For each zone ofImage A, a corresponding zone of Image B is also selected. Due to themathematics of convolution described below, the zones in Image B canideally be larger (in both width and height) than the correspondingzones of Image A. At minimum, the zones of Image B can be larger thanthose in Image A by as many pixels as it takes to adequately sample theresolution of the two optical systems (i.e., the instrumental pointspread function), but at a maximum they can be as large at the entiretyof Image B. In some embodiments, the regions of Image A and the regionsof Image B are the same dimensions, but in this case normalizedcross-correlation might not be ideal. To test for a positive match, eachzone of Image B should contain the same features of the slide incorresponding zone in Image A.

For each test zone, Z₁, centered at position (u, v) in Image A,Cross-Correlation image CC(u, v) of Z₁ with the corresponding zone, Z₂,of Image B is computed

${C\; {C\left( {u,v} \right)}} = {{{Z_{2}\left( {x,y} \right)}*{Z_{1}\left( {u,v} \right)}} = {\sum\limits_{x,y}{{Z_{2}\left( {x,y} \right)}{Z_{1}\left( {{x - u},{y - v}} \right)}}}}$

and also the Normalized-Cross-correlation image NCC(u, v):

${N\; C\; {C\left( {u,v} \right)}} = \frac{\sum\limits_{x,y}\left\lbrack {\left\lbrack {{Z_{2}\left( {x,y} \right)} - {\overset{\_}{Z}}_{2{({u,v})}}} \right\rbrack \left\lbrack {{Z_{1}\left( {{x - u},{y - v}} \right)} - {\overset{\_}{Z}}_{1}} \right\rbrack} \right\rbrack}{\left\{ {\sum\limits_{x,y}{\left\lbrack {{Z_{2}\left( {x,y} \right)} - {\overset{\_}{Z}}_{2{({u,v})}}} \right\rbrack^{2}{\sum\limits_{x,y}\left\lbrack {{Z_{1}\left( {{x - u},{y - v}} \right)} - {\overset{\_}{Z}}_{1}} \right\rbrack^{2}}}} \right\}^{0.5}}$

where Z _(2(u,v)) is the mean of Z₂(x, y) over the zone Z₁ when it is atposition (u, v), and Z ₁ is the mean of zone Z₁.

The CC (u, v) image can be thought of as a measure of the squaredEuclidean distance between Z₁ and Z_(z) at each offset (u, v). The peakpixel in CC(u, v) (i.e., the highest correlation value) represents theposition with the greatest similarity of the two zones as well as thetranslational offset of that feature within CC(u, v) that is the bestmatch of Z₁ to Z₂. The CC(u, v) peak value can have a wide range ofvalues and will vary with the total image energy ΣZ² (x, y) in each zoneand also with the size of features. A cross-correlation can be useful ifImage A and Image B were captured with the same imaging modality(ideally with the same instrument) as it allows one to quantifybrightness changes between the features of Image A and the features ofImage B.

The NCC(u, v) image, on the other hand, is insensitive to differences inthe overall brightness of Z₁ and Z₂ due to its normalization by Z_(2(u,v)) and Z ₁. This is especially useful if Image A and Image B arecaptured with different modalities and the intensity scale calibrationbetween Image A and Image B is either unknown or uncertain. The peak inthe NCC image can be thought of as a measure of the maximum similarly ofthe staining pattern of Z₁ with respect to Z₂, independent of theoverall brightness and contrast of the two zones. Due to normalization,its value is constrained to be in the range from 0.0 to 1.0 inclusive. ANCC image peak value of 1.0 indicates and exact match, a value of 0.0indicates anti-correlation. Thus, the NCC peak value can be used as arobust statistical measure of the similarity of the two imaging zones.

2. Additional Aspects of Cross-Correlation

In some embodiments, the step of identifying the first imaging zone inthe second imaging signal in step (e) is the same as thecross-correlation step generating the peak cross-correlation in step (f)in paragraph [0019] above. In some instances, the step of identifyingthe first imaging zone in the second imaging signal in step (e) isdifferent from the cross-correlation step generating the peakcross-correlation in step (f) in paragraph [0019] above.

In some embodiments, the cross-correlation is a normalizedcross-correlation. In some embodiments, the normalized cross-correlationvalue is closer to 1 for a candidate imaging method or candidate imagingreagent that is validated, representing that the methods produce thesame pattern in staining, considering both spatial layout and intensity.In some embodiments, the highest value from the normalizedcross-correlation is at least 0.5, 0.6, 0.7, 0.75, 0.8, 0.85, 0.9, 0.91,0.92, 0.93, 0.94, 0.95, 0.96. 0.97, 0.98, 0.99. In some embodiments thehighest value from the normalized cross-correlation is 1.0. It can beespecially useful to use the normalized cross-correlation when comparingdifferent imaging methods where the imaging values can be on differentscales. For example, two different imaging methods can be compared witha known, standard imaging method. If a first image obtained from a firstcandidate imaging method has the highest correlation value of 0.93 withthe image obtained from the standard imaging method and a second imageobtained from a second candidate imaging method has the highestcorrelation value of 0.90, the first image is a better match with thesecond image, indicating that the first imaging method provides a closerrepresentation to the known, standard imaging method than the secondimaging method. Based on this quantitative outcome of thecross-correlation values, one can decide on a threshold or tolerancelevel that constitutes a valid image. This threshold or tolerance levelcan be determined by applying the method described herein to a set oftest images, ordering the images by the cross-correlation value orpercent match value, inspecting the images in the order from a lowerrank to determine the point at which the image becomes acceptable (or ina reversed order from a higher rank), and then selecting thecross-correlation value or percent match value of the image at thatpoint as a threshold or tolerance level.

A user, however, does not need to normalize the cross-correlation inorder to obtain useful information from the comparison. Thus, thecross-correlation can be unnormalized. In order to validate a candidateimaging method or candidate imaging reagent, the unnormalizedcross-correlation would show a higher value. Whether normalized or not,a higher cross-correlation value represents a greater match between thestaining patterns of the two images with respect to both the spatialdistribution of the features in the imaging zones used and similarity ofthe intensities of those features. The unnormalized cross-correlationcan be higher compared to a second imaging zone, compared to a differentcandidate imaging method or candidate imaging reagent (for example,candidate imaging method A and candidate imaging method B compared toeach other), or it can be higher than a negative control or relativelyhigh compared to a range between a negative control and a positivecontrol (at least 50%, 60%, 70%, 80%, or 90% of the difference betweenthe negative control and the positive control).

D. Method of Multiplexed Imaging and/or Exchange Imaging

New methods of performing multiplexed imaging and/or exchange imagingare available in the art. These methods, described in US PublicationNos. US20160319328 and US20180164308, which are incorporated herein byreference for their description of multiplexed imaging and exchangeimaging throughout the applications, can optionally be used as one ormore of the methods for comparison according to the methods herein. Thepresent methods are useful for comparing between multiplexed imagingand/or exchange imaging reagents and/or methods, as well as betweenthese methods and other imaging methods.

In some embodiments, the biological sample is contacted with at leasttwo types of target-specific binding partners of different specificity.In some embodiments, in at least two target-specific binding partners ofthe same specificity are linked to different docking strands. In someembodiments, at least one nucleic acid strand linked to a targetspecific binding partner is a docking strand. In some embodiments, atleast one nucleic acid strand linked to a target specific bindingpartner is a primer strand for amplification of docking strands. In someembodiments, the method includes amplification.

In some embodiments, the imager strands for the first imaging method arecapable of binding a docking strand directly. In some embodiments, theimager strands for the first imaging method are capable of binding thedocking strand indirectly. In some embodiments, the imager strands forthe second imaging method are capable of binding a docking stranddirectly, while in others indirectly.

A variety of secondary imaging methods and reagents can be used as abasis for the comparison. In some situations, the second imaging methodemploys a secondary binding partner for the target-specific bindingpartner. In some instances, the secondary binding partner is added afterthe first imaging step. In some embodiments, the secondary bindingpartner is added after the target-specific binding partner, but beforethe first imaging step. In some embodiments, the secondary bindingpartner is added in a single staining step along with thetarget-specific binding partner.

A secondary binding partner can be a secondary antibody or antigenbinding fragment thereof. A secondary binding partner can also be anaptamer, protein A, protein G, tertiary antibodies or antigen-bindingfragments thereof, etc. Likewise, imager strands could also be labeledwith fluorophores, haptens, small molecules, or proteins and a secondarybinding partner could be a label-specific binding partner.

In some instances, the method includes removing the signal of the boundlabeled imager strands from the docking strands after generating thefirst imaging signal. Removing the signal of the bound labeled imagerstrand can proceed in different ways: including by (i) inactivating thelabel (for example, photobleaching), (ii) by removing the label from theimager strand (for example, by cleaving a photocleavable linkerattaching the label to the imager strand), or (iii) removing the boundlabeled imager strands from the docking strands after generating thefirst imaging signal (by washing them away under appropriate conditions,by degrading or cleaving the imager strands so that they wash away,etc.).

In some embodiments, the labeled imager strands for the first imagingmethod comprise a fluorescent label. In some embodiments, at least onelabel is a fluorescent, enzymatic, or chromogenic label. In someembodiments, the method comprises at least one of fluorescencemicroscopy, brightfield microscopy, electron microscopy, or massspectrometry imaging. In some embodiments, the labeled imager strandsfor the second imaging method comprise a fluorescent label.

Mass spectrometry imaging multiplexing can be performed using variousheavy metal isotopes conjugated to antibodies for targets of interest.In such embodiments, the label can comprise a heavy metal isotope. Massspectrometry, such as imaging mass cytometry and matrix assisted laserdesorption/ionization imaging mass spectrometry (MALDI IMS), can beperformed on tissue samples by ablating individual portions of a tissuesample and using mass spectrometry to determine if the heavy-metalisotope label is present in that individual portion of a tissue sample.See Giesen et aL, Highly Multiplexed Imaging of Tumor Tissues withSubcellular Resolution by Mass Cytometry, Nature Methods 11(4):417-422(2014); Norris et al., Imaging Mass Spectrometry. A New Tool forPathology in a Molecular Age, Proteomics Clin Appl. 7(0):733-738 (2013).

E. Optionally Employing a Second or Subsequent Imaging Zone

In some modes, the method can comprise cross-correlating only the firstimaging zone. In some modes, the method can comprise performing across-correlation on a second or subsequent imaging zone.

Thus, when a user desires to compare more than simply a first imagingzone across at least to imaging signals, the method can comprisesidentifying a second imaging zone in at least one of the first imagingsignal and the second imaging signal. Comparing between two zones canprovide an additional level of information as a user would not expect ahigh cross-correlation between a first imaging zone and a second imagingzone, whether within a single sample or different samples. This can beuseful in many respects, but especially when a user relies onunnormalized cross-correlations.

A two zone method can further comprise comparing (i) the first imagingzone in the first imaging signal to the second imaging zone in thesecond imaging signal; (ii) the first imaging zone in the first imagingsignal to the second imaging zone in the first imaging signal; (iii) thefirst imaging zone in the second imaging signal to the second imagingzone in the second imaging signal, by performing a cross-correlation andmeasuring the peak cross-correlation, wherein this cross-correlationserves as a negative control. More than one additional zone can beidentified and cross-correlated as a negative control.

In some embodiments, the cross-correlation between the first imagingzone in the first imaging signal and the second imaging signal is higherthan the negative control (at least 50%, 60%, 70%, 80%, or 90% of thedifference between the negative control and the positive control).

II. Uses for this Method of Comparison

The present methods of comparison are useful for comparing differentimaging methods and different reagents. The methods can be applied tovalidating an imaging method relative to another imaging method, or totest different imaging reagents in comparison to each other.

In some instances, the first imaging method or the first imaging reagentis the candidate imaging method or candidate imaging reagent. This isoften, but not always, a fluorogenic method that relies on an imagerstrand bearing a fluorogenic label. In some instances, the secondimaging method or the second imaging reagent is the candidate imagingmethod or candidate imaging reagent. This provides the capacity toevaluate a candidate imaging method or reagent that uses a secondarybinding partner (such as an antibody or antigen binding part thereof) aspart of the imaging process. The second imaging method or second imagingreagent, however, can also rely on an imager strand and can, optionally,include a fluorophore as its label. Thus, method can comprisescontacting the biological sample with labeled imager strands for asecond imaging method, wherein the labeled imager strands are capable ofbinding a docking strand, directly or indirectly.

The method can be used to compare two different imaging labels and/orimaging methods. In such a method, the first imaging reagent comprises afirst label and a second imaging reagent comprising a second label. Insome embodiments, the first label is one label from Table and whereinthe second label is a different label from Table 1. In some embodiments,the first label is one of a fluorescent, enzymatic, or chromogenic labeland wherein the second label is a different one of a fluorescent,enzymatic, or chromogenic label and wherein the second label.

TABLE 1 Labels for Imaging Mass Tag (such as a Chromogenic Heavy MetalIsotope) Contrast Agent Metal or Metal-Based Enzymatic Metal-OrganicCompound Fluorescent Nanoparticle Isotope Quantum Dot LuminescentPhosphorescent Lanthanide SERS

In some embodiments, the labels being compared can each be one of thesame group of labels. In some embodiments, one fluorescent label can becompared to another fluorescent label; one enzymatic label can becompared to another enzymatic label; or one chromogenic label comparedto another chromogenic label, etc. for all of the label types in Table1.

The two imaging methods can comprise different ones of fluorescencemicroscopy, brightfield microscopy, electron microscopy, massspectrometry imaging, Raman imaging, surface enhanced Raman (SERs),atomic force microscopy (AFM), phase contrast imaging, X-ray tomography,multiphoton microscopy, scanning probe microscopy, infrared microscopy,or ultraviolet microscopy.

For example, the method can be used to compare a chromogenic imagingmethod with a fluorescent imaging method. Or it can be used to compare achromogenic imaging method with a mass spectrometry imaging method.

In some embodiments, different docking strands can be compared to eachother. In some embodiments, whether to use a primer strand can beevaluated. In such embodiments, one imaging method employs a primerstrand and the other imaging method does not employ a primer strand.Likewise, a user can evaluate the impact of amplification. In someembodiments, one imaging method employs amplification and other imagingmethod does not employ amplification.

The impact of removing unbound target-specific binding partners can beevaluated. In such a situation, one imaging method removes unboundtarget-specific binding partners and the other imaging method does notremove unbound target-specific binding partners. The presence of unboundlabeled imager strands can also be assessed. In such a method, oneimaging method removes unbound labeled imager strands and the otherimaging method does not remove unbound labeled imager strands.

In some embodiments, one imaging reagent includes a labeled imagerstrand capable of binding a docking strand indirectly and the otherimaging reagent includes a labeled imager strand capable of binding adocking strand directly.

In some embodiments, the method of validating a candidate imaging methodor candidate imaging reagent is for use in evaluating a biologicalsample for the presence of a single target. In some embodiments, themethod of validating a candidate imaging method or candidate imagingreagent is for use in evaluating a biological sample for the presence ofmultiple targets.

Various forms of multiplexing or exchange imaging can be evaluatedduring this comparison. For example, multiplexing could be done at anystaining step (before the first imaging method or after the firstimaging method), with or without exchange. Exchange imaging could bedone between the first imaging method and the second imaging method.Exchange could also be done within the first imaging method, and/orwithin the second imaging method.

Therefore, this technique provides a powerful tool for comparing andvalidating different imaging methods and reagents.

EXAMPLES Example 1. Fluorescent Imaging of CD3

Fluorescent imaging of CD3 was conducted on a biological sample.

Formalin-fixed paraffin-embedded (FFPE) lung tissue was dewaxed andantigen-retrieved using a Lab Vision PT-module and pH 6 buffer. Thetissue sample was blocked in a solution containing BSA and Triton-X 100for 1.5 hours. Tissue was stained with a rabbit anti-CD3e primaryantibody conjugated to docking strand D1 for 1 hour at room temperaturein a humidity chamber. The tissue section was then washed with 1×PBS. Asolution containing a circular DNA strand with complementarity to D1 wasadded to the sample and incubated for 25 minutes at room temperature.Following washing steps, amplification of the docking strands wascarried out by applying a solution containing dNTPs and phi29 DNAPolymerase in 1× polymerase reaction buffer (New England Biolabs) andincubating for 2 hours at 30° C. The tissue was washed and DAPI wasapplied to stain nuclei.

A fluorescence microscope was used to image the tissue section in theDAPI and Cy5 channels to serve as a blank. An imager strand (I1),comprising a red fluorophore attached to DNA that includes a domaincomplementary to a docking strand D1, was added to the prepared tissuesection and allowed to hybridize for 25 minutes at room temperature.Sections were washed to remove unbound I1. Then, fluorescence imageswere captured in the DAPI and Cy5 channels using a 20× objective and 10%Sola lamp power. Exposure times were at 100 ms for the DAPI channel and500 ms for the Cy5 channel.

FIG. 1 provides a first imaging signal from a fluorescent stain of abiological sample.

Example 2. Chromogenic Brightfield Imaging of CD3

The same biological sample was used for chromogenic brightfield imagingof CD3.

Using the sample at the end of Example 1, the imager strand I1 was thenremoved by applying a solution of USER enzyme to the tissue section for15 minutes at room temperature and washing with 1×PBS.

The tissue section was incubated with a peroxidase suppressor for 15minutes at room temperature, washed, and incubated with a secondaryantibody bound to horseradish peroxidase (HRP) for 1.5 hours. The tissuewas washed and a mixture of 3,3′-diaminobenzidine (DAB) chromogen andsubstrate was added and allowed to incubate for 15 minutes, followed bya final washing step. Finally, the sample was imaged using brightfieldillumination on an EVOS microscope with 10×, 20×, and 40× magnification

FIG. 2 shows a second imaging signal from a chromogenic stain of thesame biological sample as shown in FIG. 1.

Example 3. Comparing the Fluorescent Image and the ChromogenicBrightfield Image

This example was created using a general-purpose computer (Mac BookPro), with computer code written in C++ and the use of some functionsfrom the open source image processing library OpenCV (opencv.org). Thetype of computer used, the choice of coding language and the use of theparticular library are all optional: any practical computer could beused, the code could be written in another language, and the use of thelibrary is not required.

The chromogenic brightfield image (3×8 bit RGB) of FIG. 2 underwentimage processing steps to result in FIG. 3:

-   -   a. Converted the RGB image to HSV    -   b. Created a mask image selecting Hue values in the range 0 to        500    -   c. Eroded the mask with a 2×2 rectangular kernel operator, one        iteration.    -   d. Dilated the mask with a 3×3 cross (+) kernel operator, 8        iterations    -   e. Inverted the Value channel (255−V) and multiply by the mask    -   f. Found the minimum (0) and maximum values (238) and scale the        intensities in this range to 0 to 255.    -   g. Calculated the Affine transform for a rotation of −0.331°        about the center of the image, scaling the dimensions of the        image by 1.36174.

$\quad\begin{bmatrix}1.36173 & {- 0.0078668} & 33.1575 \\0.0078668 & 1.36172 & {- 40.4029} \\0 & 0 & 1\end{bmatrix}$

-   -   -   The scale factor and rotation angle, provided as inputs,            were measured by the user by identifying corresponding            features in the input images, and finding the ratio of the            distance between them to determine the scale and finding the            difference in their angle with respect to the x axis            (arctangent) for the angle of rotation.

Thus, FIG. 3 shows the second imaging signal shown in FIG. 2 after imageadjustments steps to align with the first imaging signal in FIG. 1.

A subsequent (optional) cropping step was performed match the bounds ofthe first imaging signal image to the bounds of the second imagingsignal produced in the step above.

-   -   a. Calculated the normalized cross-correlation of the entire        aligned second imaging signal image (FIG. 3) to the first        imaging signal (FIG. 1).    -   b. Found the position of the peak (0.577) in the NCC image is at        pixel position (677, 172)    -   c. Cropped the first imaging signal (FIG. 1) to a rectangle with        top left corner at (677,172), width of 14020, and height of        11438 pixels (FIG. 4).

Thus, FIG. 4 shows the first imaging signal from FIG. 1 after croppingto the bounds of the processed second imaging signal of FIG. 2.

Next, example imaging zones 1000 pixels wide and 1000 pixels highcentered on signals of interest identified by the user were selectedfrom the first imaging signal (FIG. 4) centered on signals of interest.The zones were expanded by 100 pixels on all 4 sides (width=1200,height=1200 pixels) for use as the corresponding imaging zones of thesecond imaging signal (FIG. 3).

FIG. 5 shows a first example imaging zone (A) and a second exampleimaging zone (B) overlaid on a portion of the chromogenic stain of FIG.2. The black lines at A and B show the bounds of the example imagingzones in the first imaging signal (fluorescent image) and white lines atA and B show the bounds of the example imaging zone in the secondimaging signal (chromogenic stain).

FIGS. 6A-6D show details of the example imaging zones A and B in thealigned images of FIG. 3 and FIG. 4.

The normalized cross-correlation (NCC) image for all possiblecombinations of the example imaging zones from the first imaging signalwith the example imaging zones of the second imaging signal wascalculated. FIG. 7-D shows pseudo color representations where bluerepresents no correlation and red 100% correlation.

The peak value of each NCC image was found and is listed in Table 2. Asexpected, when the example imaging zone A from the fluorescent image wascompared to the corresponding imaging zone A from the adjustedchromogenic image which contains the same staining pattern of the sample(positive test), the NCC image peak was quite high (>50%). On the otherhand, when the same example imaging zone A from the fluorescent imagewas compared to the example imaging zone B from the adjusted chromogenicimage which contains a different staining pattern (negative test), theNCC image peak was quite low (<10%).

TABLE 2 Normalized Cross-Correlation Values Correlation NCC Image Peak Avs. A 0.728 B vs. B 0.682 A vs. B 0.057 B vs. A 0.048

This demonstrates that after first processing an image to align itssignals with that of another image and then calculating the normalizedcross-correlation of imaging zones of one with imaging zones of theother is an effective way to quantitatively compare the similarity ofthe signal pattern found in the different modalities.

Example 4. Additional Embodiments

The following numbered items provide additional support for anddescriptions of the embodiments herein.

Item 1. A quantitative method of validating at least one candidateimaging method or candidate imaging reagent for use in evaluating abiological sample for the presence of one or more targets comprising:

-   -   a. obtaining a first imaging signal using a first imaging method        and/or first imaging reagents comprising        -   i. contacting a biological sample with one or more            target-specific binding partners, wherein each            target-specific binding partner is linked to a nucleic acid            strand and wherein target-specific binding partners of            different specificity, if present, are linked to different            nucleic acid strands, wherein the nucleic acid strand is            either a docking strand or a primer strand for amplification            of docking strands;        -   ii. contacting the biological sample with labeled imager            strands for a first imaging method, wherein the labeled            imager strands are capable of binding a docking strand,            directly or indirectly,        -   iii. generating a first imaging signal;    -   b. optionally removing the bound labeled imager strands from the        docking strands;    -   c. obtaining a second imaging signal using a second imaging        method and/or second imaging reagents comprising contacting the        biological sample with either (1) labeled imager strands,        wherein the labeled imager strands are capable of binding a        docking strand, directly or indirectly, or (2) a secondary        binding partner for the target-specific binding partner,    -   wherein the second imaging method and/or the second imaging        reagent is different the first imaging method and/or first        imaging reagent;    -   d. optionally aligning the first imaging signal and the second        imaging signal to adjust for signal orientation, image parity,        scale, rotation, and/or translation mismatch,    -   e. identifying a first imaging zone in both the first imaging        signal and second imaging signal, wherein the first imaging zone        in both imaging signals represents the same location in the        biological sample;    -   f. comparing the first imaging zone in the first imaging signal        and the first imaging zone in the second imaging signal by        performing a cross-correlation.

Item 2. A quantitative method of validating at least one candidateimaging method or candidate imaging reagent for use in evaluating abiological sample for the presence of one or more targets comprising:

-   -   a. obtaining a first imaging signal using a first imaging method        and/or first imaging reagents,    -   b. obtaining a second imaging signal using a second imaging        method and/or second imaging reagents,    -   wherein the second imaging method and/or the second imaging        reagent is different the first imaging method and/or first        imaging reagent;    -   c. optionally aligning the first imaging signal and the second        imaging signal to adjust for signal orientation, image parity,        scale, rotation, and/or translation mismatch,    -   d. identifying a first imaging zone in both the first imaging        signal and second imaging signal, wherein the first imaging zone        in both imaging signals represents the same location in the        biological sample;    -   e. comparing the first imaging zone in the first imaging signal        and the first imaging zone in the second imaging signal by        performing a cross-correlation.

Item 3. The method of items 1-2, wherein the cross-correlation comprisesgenerating an image.

Item 4. The method of any one of items 1-3, wherein thecross-correlation comprises generating numerical data.

Item 5. The method of any one of items 1-4, wherein thecross-correlation comprises identifying the peak cross-correlationbetween the first imaging zone in the first imaging signal and the firstimaging zone in the second imaging signal.

Item 6. The method of any one of items 1-5, wherein thecross-correlation comprises evaluating the breadth of the peakcross-correlation.

Item 7. The method of item 1, wherein the step of identifying the firstimaging zone in the second imaging signal in step (e) is the same stepas the cross-correlation step in step (f).

Item 8. The method of any one of items 1-7, wherein thecross-correlation is a normalized cross-correlation.

Item 9. The method of any one of items 1-8, wherein the normalizedcross-correlation value is closer to 1 for a candidate imaging method orcandidate imaging reagent that is validated.

Item 10. The method of any one of items 1-9, wherein the highest valuefrom the normalized cross-correlation is at least 0.50.

Item 11. The method of any one of items 1-10, wherein thecross-correlation is unnormalized.

Item 12. The method of item 11, wherein the nonnormalizedcross-correlation value is higher for a candidate imaging method orcandidate imaging reagent that is validated.

Item 13. The method of any one of items 1-12, wherein thecross-correlation is computed in a spatial domain.

Item 14. The method of any one of items 1-13, wherein thecross-correlation is computed in a frequency domain.

Item 15. The method of any one of items 1-14, wherein the methodcomprises aligning the first imaging signal and the second imagingsignal to adjust for signal orientation, image parity, scale, rotation,and/or translation mismatch.

Item 16. The method of item 15, wherein the method comprises aligningthe first imaging signal and the second imaging signal to adjust forsignal orientation.

Item 17. The method of any one of items 15-16, wherein the methodcomprises aligning the first imaging signal and the second imagingsignal to adjust for image parity.

Item 18. The method of any one of items 15-17, wherein the methodcomprises aligning the first imaging signal and the second imagingsignal to adjust for image scale.

Item 19. The method of any one of items 15-18, wherein the methodcomprises aligning the first imaging signal and the second imagingsignal to adjust for image rotation.

Item 20. The method of any one of items 15-19, wherein the methodcomprises aligning the first imaging signal and the second imagingsignal to adjust for translation mismatch.

Item 21. The method of any one of items 15-20, wherein the methodfurther comprises intensity scaling.

Item 22. The method of any one of items 15-21, wherein the methodfurther comprises morphology operations.

Item 23. The method of any one of items 1-22, wherein the biologicalsample is contacted with at least two types of target-specific bindingpartners of different specificity.

Item 24. The method of any one of items 1-23, wherein at least twotarget-specific binding partners of the same specificity are linked todifferent docking strands.

Item 25. The method of any one of items 1-24, wherein at least onenucleic acid strand linked to a target specific binding partner is adocking strand.

Item 26. The method of any one of items 1-25, wherein at least onenucleic acid strand linked to a target specific binding partner is aprimer strand for amplification of docking strands.

Item 27. The method of any one of items 1-26, wherein method includesamplification.

Item 28. The method of any one of items 1-27, wherein the imager strandsfor the first imaging method are capable of binding a docking stranddirectly.

Item 29. The method of any one of items 1-28, wherein the imager strandsfor the first imaging method are capable of binding the docking strandindirectly.

Item 30. The method of any one of items 1-29, wherein the imager strandsfor the second imaging method are capable of binding a docking stranddirectly.

Item 31. The method of any one of items 1-30, wherein the imager strandsfor the second imaging method are capable of binding the docking strandindirectly.

Item 32. The method of any one of items 1-31, wherein the second imagingmethod employs a secondary binding partner for the target-specificbinding partner.

Item 33. The method of item 32, wherein the secondary binding partner isadded after the first imaging step.

Item 34. The method of items 33, wherein the secondary binding partneris added after the target-specific binding partner, but before the firstimaging step.

Item 35. The method of any one of items 1-34, wherein the methodincludes removing the signal of the bound labeled imager strands fromthe docking strands after generating the first imaging signal.

Item 36. The method of any one of items 1-35, wherein removing thesignal comprises inactivating the label.

Item 37. The method of any one of items 1-36, wherein removing thesignal comprises removing the label from the imager strand.

Item 38. The method of any one of items 1-37, wherein removing thesignal comprises removing the labeled imager strand from the dockingstrand.

Item 39. The method of any one of items 1-38, wherein the method doesnot include removing the signal of the bound labeled imager strands fromthe docking strands after generating the first imaging signal.

Item 40. The method of any one of items 1-39, wherein the labeled imagerstrands for the first imaging method comprise a fluorescent label.

Item 41. The method of any one of items 1-40, wherein at least one labelis a fluorescent, enzymatic, or chromogenic label.

Item 42. The method of any one of items 1-41, wherein the methodcomprises at least one of fluorescence microscopy, brightfieldmicroscopy, electron microscopy, or mass spectrometry imaging.

Item 43. The method of any one of items 1-42, wherein the labeled imagerstrands for the second imaging method comprise a fluorescent label.

Item 44. The method of any one of items 1-43, wherein the first imagingzone comprises the entire first imaging signal.

Item 45. The method of any one of items 1-44, wherein the second imagingzone comprises the entire second imaging signal.

Item 46. The method of any one of items 1-45, wherein the methodcompares two candidate imaging methods.

Item 47. The method of any one of items 1-46, wherein the methodcompares two candidate imaging reagents.

Item 48. The method of any one of items 1-47, wherein the methodcompares more than two candidate imaging methods.

Item 49. The method of any one of items 1-48, wherein the methodcompares more than two candidate imaging reagents.

Item 50. The method of any one of items 1-48, wherein the first imagingmethod or the first imaging reagent is the candidate imaging method orcandidate imaging reagent.

Item 51. The method of any one of items 1-50, wherein the second imagingmethod or the second imaging reagent is the candidate imaging method orcandidate imaging reagent.

Item 52. The method of any one of items 1-50, wherein the methodcomprises contacting the biological sample with labeled imager strandsfor a second imaging method, wherein the labeled imager strands arecapable of binding a docking strand, directly or indirectly.

Item 53. The method of any one of items 1-52, wherein the methodcomprises contacting the biological sample with a secondary bindingpartner for the target-specific binding partner.

Item 54. The method of item 53, wherein the secondary binding partnerfor the target-specific binding partner is a secondary antibody orantigen-binding fragment thereof.

Item 55. The method of any one of items 1-54, wherein the methodcomprises comparing a first imaging reagent comprising a first label anda second imaging reagent comprising a second label.

Item 56. The method of item 55, wherein the first label is one of thelabels in Table 1 and wherein the second label is a different one thelabels in Table 1.

Item 57. The method of any one of items 1-56, wherein one imaging methodcomprises one fluorescence microscopy, brightfield microscopy, electronmicroscopy, mass spectrometry imaging, Raman imaging, surface enhancedRaman (SERs), atomic force microscopy (AFM), phase contrast imaging,X-ray tomography, multiphoton microscopy, scanning probe microscopy,infrared microscopy, or ultraviolet microscopy.

Item 58. The method of any one of items 1-57, wherein one imagingreagent comprises a first docking strand and the other imaging reagentcomprises a second docking strand.

Item 59. The method of any one of items 1-58, wherein one imaging methodemploys a primer strand and the other imaging method does not employ aprimer strand.

Item 60. The method of any one of items 1-59, wherein one imaging methodemploys amplification and the other imaging method does not employamplification.

Item 61. The method of any one of items 1-60, wherein one imaging methodremoves unbound target-specific binding partners and the other imagingmethod does not remove unbound target-specific binding partners.

Item 62. The method of any one of items 1-61, wherein one imaging methodremoves unbound labeled imager strands and the other imaging method doesnot remove unbound labeled imager strands.

Item 63. The method of any one of items 1-62, wherein the first imagingreagent includes a labeled imager strand capable of binding a dockingstrand indirectly and the other imaging reagent includes a labeledimager strand capable of binding a docking strand directly.

Item 64. The method of any one of items 1-63, wherein the method ofvalidating a candidate imaging method or candidate imaging reagent isfor use in evaluating a biological sample for the presence of a singletarget.

Item 65. The method of any one of items 1-64, wherein the method ofvalidating a candidate imaging method or candidate imaging reagent isfor use in evaluating a biological sample for the presence of multipletargets.

Item 66. The method of any one of items 1-65, wherein the method furthercomprises identifying a second imaging zone in at least one of the firstimaging signal and the second imaging signal.

Item 67. The method of item 66, wherein the method further comprisescomparing

-   -   a. the first imaging zone in the first imaging signal to the        second imaging zone in the second imaging signal;    -   b. the first imaging zone in the first imaging signal to the        second imaging zone in the first imaging signal;    -   c. the first imaging zone in the second imaging signal to the        second imaging zone in the second imaging signal,        by performing a cross-correlation and measuring the peak        cross-correlation, wherein this cross-correlation serves as a        negative control.

Item 68. The method of any one of items 66-67, wherein more than oneadditional zone is identified and cross-correlated as a negativecontrol.

Item 69. The method of any one of items 66-68, wherein thecross-correlation is a unnormalized cross-correlation.

Item 70. The method of any one of items 66-69, wherein thecross-correlation between the first imaging zone in the first imagingsignal and the second imaging signal is higher than the negative control(at least 50%, 60%, 70%, 80%, or 90% of the difference between thenegative control and the positive control).

Item 71. The method of any one of items 1-70, wherein at least oneimaging zone comprises areas of both signal and background.

Item 72. The method of any one of items 1-71, wherein at least oneimaging zone contains at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,14, 15 imaging elements.

Item 73. The method of any one of items 1-72, wherein one imaging zonecomprises the strongest signal from the biological sample in the firstand/or second imaging signal.

Item 74. The method of any one of items 1-73, wherein an imaging zone ischosen because it comprises the strongest signal from the biologicalsample.

Item 75. The method of any one of items 1-74, wherein at least oneimaging zone is chosen randomly.

Item 76. The method of any one of items 1-75, wherein at least oneimaging zone is at least 5, 10, 15, 20, 25, or 30 times the size of anaverage cell in the biological sample.

Item 77. The method of any one of items 1-76, wherein the first imagingzone and the second imaging zone are non-overlapping.

Item 78. The method of any one of items 1-77, wherein at least oneimaging zone in the first and second imaging signal comprise all of thesame portions of the biological sample.

Item 79. The method of any one of items 1-78, wherein the first imagingzone in the first and second imaging signal comprise 90% of the sameportions of the biological sample.

Item 80. The method of any one of items 1-79, wherein only one imagingzone is compared between the first and second imaging signals.

Item 81. The method of any one of items 1-80, wherein at least oneimaging zone in the first imaging signal is compared with the entirebiological sample in the second imaging signal.

Item 82. The method of any one of items 1-81, wherein more than twoimaging zones are used.

Item 83. The method of any one of items 2-82, wherein the first imagingmethod employs a first targeting antibody bound directly or indirectlyto a first label and the second imaging method employs a secondtargeting antibody bound directly or indirectly to a second label.

Item 84. The method of item 83, wherein the first targeting antibody isbound directly to a first label.

Item 85. The method of any one of items 83-84, wherein the secondtargeting antibody is bound directly to a second label.

EQUIVALENTS

The foregoing written specification is considered to be sufficient toenable one skilled in the art to practice the embodiments. The foregoingdescription and Examples detail certain embodiments and describe thebest mode contemplated by the inventors. It will be appreciated,however, that no matter how detailed the foregoing may appear in text,the embodiment may be practiced in many ways and should be construed inaccordance with the appended claims and any equivalents thereof.

As used herein, the term “about” refers to a numeric value, including,for example, whole numbers, fractions, and percentages, whether or notexplicitly indicated. The term about generally refers to a range ofnumerical values (e.g., +/−5-10% of the recited range) that one ofordinary skill in the art would consider equivalent to the recited value(e.g., having the same function or result). When terms such as at leastand about precede a list of numerical values or ranges, the terms modifyall of the values or ranges provided in the list. In some instances, theterm about can include numerical values that are rounded to the nearestsignificant figure.

What is claimed is:
 1. A quantitative method of validating at least one candidate imaging method or candidate imaging reagent for use in evaluating a biological sample for the presence of one or more targets comprising: a. obtaining a first imaging signal using a first imaging method and/or first imaging reagents comprising: i. contacting a biological sample with one or more target-specific binding partners, wherein each target-specific binding partner is linked to a nucleic acid strand and wherein target-specific binding partners of different specificity, if present, are linked to different nucleic acid strands, wherein the nucleic acid strand is either a docking strand or a primer strand for amplification of docking strands; ii. contacting the biological sample with labeled imager strands for a first imaging method, wherein the labeled imager strands are capable of binding a docking strand, directly or indirectly, iii. generating a first imaging signal; b. optionally removing the bound labeled imager strands from the docking strands; c. obtaining a second imaging signal using a second imaging method and/or second imaging reagents comprising contacting the biological sample with either (1) labeled imager strands, wherein the labeled imager strands are capable of binding a docking strand, directly or indirectly, or (2) a secondary binding partner for the target-specific binding partner, wherein the second imaging method and/or the second imaging reagent is different the first imaging method and/or first imaging reagent; d. optionally aligning the first imaging signal and the second imaging signal to adjust for signal orientation, image parity, scale, rotation, and/or translation mismatch, e. identifying a first imaging zone in both the first imaging signal and second imaging signal, wherein the first imaging zone in both imaging signals represents the same location in the biological sample; f. comparing the first imaging zone in the first imaging signal and the first imaging zone in the second imaging signal by performing a cross-correlation.
 2. A quantitative method of validating at least one candidate imaging method or candidate imaging reagent for use in evaluating a biological sample for the presence of one or more targets comprising: a. obtaining a first imaging signal using a first imaging method and/or first imaging reagents, b. obtaining a second imaging signal using a second imaging method and/or second imaging reagents, wherein the second imaging method and/or the second imaging reagent is different the first imaging method and/or first imaging reagent; c. optionally aligning the first imaging signal and the second imaging signal to adjust for signal orientation, image parity, scale, rotation, and/or translation mismatch, d. identifying a first imaging zone in both the first imaging signal and second imaging signal, wherein the first imaging zone in both imaging signals represents the same location in the biological sample; e. comparing the first imaging zone in the first imaging signal and the first imaging zone in the second imaging signal by performing a cross-correlation.
 3. The method of claim 2, wherein the cross-correlation comprises generating an image.
 4. The method of claim 2, wherein the cross-correlation comprises generating numerical data.
 5. The method of claim 2, wherein the cross-correlation comprises identifying the peak cross-correlation between the first imaging zone in the first imaging signal and the first imaging zone in the second imaging signal.
 6. The method of claim 2, wherein the cross-correlation comprises evaluating the breadth of the peak cross-correlation.
 7. The method of claim 2, wherein the step of identifying the first imaging zone in the second imaging signal in step (d) is the same step as the cross-correlation step in step (e).
 8. The method of claim 2, wherein the cross-correlation is a normalized cross-correlation.
 9. The method of claim 2, wherein the normalized cross-correlation value is closer to 1 for a candidate imaging method or candidate imaging reagent that is validated.
 10. The method of claim 2, wherein the highest value from the normalized cross-correlation is at least 0.50.
 11. The method of claim 2, wherein the cross-correlation is unnormalized.
 12. The method of claim 11, wherein the nonnormalized cross-correlation value is higher for a candidate imaging method or candidate imaging reagent that is validated.
 13. The method of claim 2, wherein the cross-correlation is computed in a spatial domain.
 14. The method of claim 2, wherein the cross-correlation is computed in a frequency domain.
 15. The method of claim 2, wherein the method comprises aligning the first imaging signal and the second imaging signal to adjust for signal orientation, image parity, scale, rotation, and/or translation mismatch.
 16. The method of claim 15, wherein the method comprises aligning the first imaging signal and the second imaging signal to adjust for signal orientation.
 17. The method of claim 15, wherein the method comprises aligning the first imaging signal and the second imaging signal to adjust for image parity.
 18. The method of claim 15, wherein the method comprises aligning the first imaging signal and the second imaging signal to adjust for image scale.
 19. The method of claim 15, wherein the method comprises aligning the first imaging signal and the second imaging signal to adjust for image rotation.
 20. The method of claim 15, wherein the method comprises aligning the first imaging signal and the second imaging signal to adjust for translation mismatch.
 21. The method of claim 15, wherein the method further comprises intensity scaling.
 22. The method of claim 15, wherein the method further comprises morphology operations.
 23. The method of claim 2, wherein the biological sample is contacted with at least two types of target-specific binding partners of different specificity.
 24. The method of claim 2, wherein the method includes removing the signal of the bound labeled imager strands from the docking strands after generating the first imaging signal.
 25. The method of claim 2, wherein removing the signal comprises inactivating the label.
 26. The method of claim 2, wherein at least one label is a fluorescent, enzymatic, or chromogenic label.
 27. The method of claim 2, wherein the method comprises at least one of fluorescence microscopy, brightfield microscopy, electron microscopy, or mass spectrometry imaging.
 28. The method of claim 2, wherein the method compares two candidate imaging methods.
 29. The method of claim 2, wherein the method compares two candidate imaging reagents.
 30. The method of claim 2, wherein the method comprises comparing a first imaging reagent comprising a first label and a second imaging reagent comprising a second label.
 31. The method of claim 2, wherein one imaging method comprises one fluorescence microscopy, brightfield microscopy, electron microscopy, mass spectrometry imaging, Raman imaging, surface enhanced Raman (SERs), atomic force microscopy (AFM), phase contrast imaging, X-ray tomography, multiphoton microscopy, scanning probe microscopy, infrared microscopy, or ultraviolet microscopy.
 32. The method of claim 2, wherein the method further comprises identifying a second imaging zone in at least one of the first imaging signal and the second imaging signal.
 33. The method of claim 32, wherein the method further comprises comparing a. the first imaging zone in the first imaging signal to the second imaging zone in the second imaging signal; b. the first imaging zone in the first imaging signal to the second imaging zone in the first imaging signal; c. the first imaging zone in the second imaging signal to the second imaging zone in the second imaging signal, by performing a cross-correlation and measuring the peak cross-correlation, wherein this cross-correlation serves as a negative control.
 34. The method of claim 33, wherein more than one additional zone is identified and cross-correlated as a negative control.
 35. The method of claim 33, wherein the cross-correlation is a unnormalized cross-correlation.
 36. The method of claim 33, wherein the cross-correlation between the first imaging zone in the first imaging signal and the second imaging signal is higher than the negative control (at least 50%, 60%, 70%, 80%, or 90% of the difference between the negative control and the positive control).
 37. The method of claim 2, wherein at least one imaging zone comprises areas of both signal and background.
 38. The method of claim 2, wherein at least one imaging zone contains at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 imaging elements.
 39. The method of claim 2, wherein one imaging zone comprises the strongest signal from the biological sample in the first and/or second imaging signal.
 40. The method of claim 2, wherein an imaging zone is chosen because it comprises the strongest signal from the biological sample.
 41. The method of claim 2, wherein at least one imaging zone is chosen randomly.
 42. The method of claim 2, wherein at least one imaging zone is at least 5, 10, 15, 20, 25, or 30 times the size of an average cell in the biological sample.
 43. The method of claim 2, wherein the first imaging zone and the second imaging zone are non-overlapping.
 44. The method of claim 2, wherein at least one imaging zone in the first and second imaging signal comprise all of the same portions of the biological sample.
 45. The method of claim 2, wherein the first imaging zone in the first and second imaging signal comprise 90% of the same portions of the biological sample.
 46. The method of claim 2, wherein only one imaging zone is compared between the first and second imaging signals.
 47. The method of claim 2, wherein at least one imaging zone in the first imaging signal is compared with the entire biological sample in the second imaging signal.
 48. The method of claim 2, wherein more than two imaging zones are used. 